A random-key genetic algorithm for the generalized traveling salesman problem

2006 ◽  
Vol 174 (1) ◽  
pp. 38-53 ◽  
Author(s):  
Lawrence V. Snyder ◽  
Mark S. Daskin
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Generalized Traveling Salesman Problem

scholarly journals An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

2017 ◽  
Vol 7 (6) ◽  
pp. 2260-2265 ◽  
Author(s):  
H. Jafarzadeh ◽  
N. Moradinasab ◽  
M. Elyasi
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Nearest Neighbor Search ◽  
Traveling Salesman ◽  
Computational Time ◽  
Test Problems ◽  
Solution Quality ◽  
Generalized Traveling Salesman Problem

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

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